Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations500
Missing cells280
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.1 KiB
Average record size in memory176.3 B

Variable types

Text2
Categorical5
Numeric12
DateTime3

Alerts

irrigation_type has 150 (30.0%) missing values Missing
crop_disease_status has 130 (26.0%) missing values Missing
farm_id has unique values Unique
yield_kg_per_hectare has unique values Unique
sensor_id has unique values Unique
latitude has unique values Unique
longitude has unique values Unique

Reproduction

Analysis started2025-06-10 21:01:42.220046
Analysis finished2025-06-10 21:02:08.143771
Duration25.92 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

farm_id
Text

Unique 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2025-06-11T00:02:08.603923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters4000
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)100.0%

Sample

1st rowFARM0001
2nd rowFARM0002
3rd rowFARM0003
4th rowFARM0004
5th rowFARM0005
ValueCountFrequency (%)
farm0005 1
 
0.2%
farm0500 1
 
0.2%
farm0001 1
 
0.2%
farm0002 1
 
0.2%
farm0485 1
 
0.2%
farm0486 1
 
0.2%
farm0487 1
 
0.2%
farm0488 1
 
0.2%
farm0489 1
 
0.2%
farm0490 1
 
0.2%
Other values (490) 490
98.0%
2025-06-11T00:02:09.420573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 699
17.5%
F 500
12.5%
A 500
12.5%
R 500
12.5%
M 500
12.5%
1 200
 
5.0%
3 200
 
5.0%
2 200
 
5.0%
4 200
 
5.0%
5 101
 
2.5%
Other values (4) 400
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 699
17.5%
F 500
12.5%
A 500
12.5%
R 500
12.5%
M 500
12.5%
1 200
 
5.0%
3 200
 
5.0%
2 200
 
5.0%
4 200
 
5.0%
5 101
 
2.5%
Other values (4) 400
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 699
17.5%
F 500
12.5%
A 500
12.5%
R 500
12.5%
M 500
12.5%
1 200
 
5.0%
3 200
 
5.0%
2 200
 
5.0%
4 200
 
5.0%
5 101
 
2.5%
Other values (4) 400
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 699
17.5%
F 500
12.5%
A 500
12.5%
R 500
12.5%
M 500
12.5%
1 200
 
5.0%
3 200
 
5.0%
2 200
 
5.0%
4 200
 
5.0%
5 101
 
2.5%
Other values (4) 400
10.0%

region
Categorical

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Central USA
109 
East Africa
107 
North India
99 
South USA
94 
South India
91 

Length

Max length11
Median length11
Mean length10.624
Min length9

Characters and Unicode

Total characters5312
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth India
2nd rowSouth USA
3rd rowSouth USA
4th rowCentral USA
5th rowCentral USA

Common Values

ValueCountFrequency (%)
Central USA 109
21.8%
East Africa 107
21.4%
North India 99
19.8%
South USA 94
18.8%
South India 91
18.2%

Length

2025-06-11T00:02:09.572262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-11T00:02:09.704111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
usa 203
20.3%
india 190
19.0%
south 185
18.5%
central 109
10.9%
east 107
10.7%
africa 107
10.7%
north 99
9.9%

Most occurring characters

ValueCountFrequency (%)
a 513
 
9.7%
t 500
 
9.4%
500
 
9.4%
S 388
 
7.3%
r 315
 
5.9%
A 310
 
5.8%
n 299
 
5.6%
i 297
 
5.6%
o 284
 
5.3%
h 284
 
5.3%
Other values (12) 1622
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5312
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 513
 
9.7%
t 500
 
9.4%
500
 
9.4%
S 388
 
7.3%
r 315
 
5.9%
A 310
 
5.8%
n 299
 
5.6%
i 297
 
5.6%
o 284
 
5.3%
h 284
 
5.3%
Other values (12) 1622
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5312
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 513
 
9.7%
t 500
 
9.4%
500
 
9.4%
S 388
 
7.3%
r 315
 
5.9%
A 310
 
5.8%
n 299
 
5.6%
i 297
 
5.6%
o 284
 
5.3%
h 284
 
5.3%
Other values (12) 1622
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5312
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 513
 
9.7%
t 500
 
9.4%
500
 
9.4%
S 388
 
7.3%
r 315
 
5.9%
A 310
 
5.8%
n 299
 
5.6%
i 297
 
5.6%
o 284
 
5.3%
h 284
 
5.3%
Other values (12) 1622
30.5%

crop_type
Categorical

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Maize
111 
Soybean
108 
Cotton
107 
Wheat
92 
Rice
82 

Length

Max length7
Median length6
Mean length5.482
Min length4

Characters and Unicode

Total characters2741
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWheat
2nd rowSoybean
3rd rowWheat
4th rowMaize
5th rowCotton

Common Values

ValueCountFrequency (%)
Maize 111
22.2%
Soybean 108
21.6%
Cotton 107
21.4%
Wheat 92
18.4%
Rice 82
16.4%

Length

2025-06-11T00:02:09.872700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-11T00:02:09.987291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
maize 111
22.2%
soybean 108
21.6%
cotton 107
21.4%
wheat 92
18.4%
rice 82
16.4%

Most occurring characters

ValueCountFrequency (%)
e 393
14.3%
o 322
11.7%
a 311
11.3%
t 306
11.2%
n 215
 
7.8%
i 193
 
7.0%
z 111
 
4.0%
M 111
 
4.0%
y 108
 
3.9%
S 108
 
3.9%
Other values (6) 563
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2741
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 393
14.3%
o 322
11.7%
a 311
11.3%
t 306
11.2%
n 215
 
7.8%
i 193
 
7.0%
z 111
 
4.0%
M 111
 
4.0%
y 108
 
3.9%
S 108
 
3.9%
Other values (6) 563
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2741
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 393
14.3%
o 322
11.7%
a 311
11.3%
t 306
11.2%
n 215
 
7.8%
i 193
 
7.0%
z 111
 
4.0%
M 111
 
4.0%
y 108
 
3.9%
S 108
 
3.9%
Other values (6) 563
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2741
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 393
14.3%
o 322
11.7%
a 311
11.3%
t 306
11.2%
n 215
 
7.8%
i 193
 
7.0%
z 111
 
4.0%
M 111
 
4.0%
y 108
 
3.9%
S 108
 
3.9%
Other values (6) 563
20.5%

soil_moisture_%
Real number (ℝ)

Distinct475
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.75014
Minimum10.16
Maximum44.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-06-11T00:02:10.172543image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.16
5-th percentile11.5895
Q117.89
median25.855
Q336.0225
95-th percentile42.6505
Maximum44.98
Range34.82
Interquartile range (IQR)18.1325

Descriptive statistics

Standard deviation10.150053
Coefficient of variation (CV)0.37943925
Kurtosis-1.2380887
Mean26.75014
Median Absolute Deviation (MAD)8.755
Skewness0.12396359
Sum13375.07
Variance103.02358
MonotonicityNot monotonic
2025-06-11T00:02:10.420886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.25 4
 
0.8%
32.56 3
 
0.6%
12.8 3
 
0.6%
27.35 2
 
0.4%
29.32 2
 
0.4%
17.33 2
 
0.4%
42.64 2
 
0.4%
18.22 2
 
0.4%
10.28 2
 
0.4%
23.35 2
 
0.4%
Other values (465) 476
95.2%
ValueCountFrequency (%)
10.16 1
0.2%
10.22 1
0.2%
10.25 1
0.2%
10.26 1
0.2%
10.27 1
0.2%
10.28 2
0.4%
10.31 1
0.2%
10.34 1
0.2%
10.45 1
0.2%
10.66 1
0.2%
ValueCountFrequency (%)
44.98 1
0.2%
44.93 1
0.2%
44.91 1
0.2%
44.69 1
0.2%
44.64 1
0.2%
44.2 1
0.2%
44.18 1
0.2%
44.16 1
0.2%
44.13 1
0.2%
43.89 1
0.2%

soil_pH
Real number (ℝ)

Distinct184
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.52398
Minimum5.51
Maximum7.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-06-11T00:02:10.770517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.51
5-th percentile5.6
Q16.03
median6.53
Q37.04
95-th percentile7.43
Maximum7.5
Range1.99
Interquartile range (IQR)1.01

Descriptive statistics

Standard deviation0.58555829
Coefficient of variation (CV)0.089754765
Kurtosis-1.2127359
Mean6.52398
Median Absolute Deviation (MAD)0.5
Skewness-0.033405268
Sum3261.99
Variance0.34287852
MonotonicityNot monotonic
2025-06-11T00:02:10.987396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.97 7
 
1.4%
6.33 7
 
1.4%
6.3 6
 
1.2%
7.04 6
 
1.2%
7.3 6
 
1.2%
6.78 6
 
1.2%
5.6 6
 
1.2%
6.36 5
 
1.0%
7.05 5
 
1.0%
6.08 5
 
1.0%
Other values (174) 441
88.2%
ValueCountFrequency (%)
5.51 5
1.0%
5.52 1
 
0.2%
5.53 2
 
0.4%
5.54 3
0.6%
5.55 1
 
0.2%
5.56 2
 
0.4%
5.57 3
0.6%
5.58 3
0.6%
5.6 6
1.2%
5.61 3
0.6%
ValueCountFrequency (%)
7.5 1
 
0.2%
7.49 5
1.0%
7.48 1
 
0.2%
7.47 5
1.0%
7.46 4
0.8%
7.45 1
 
0.2%
7.44 5
1.0%
7.43 5
1.0%
7.42 2
 
0.4%
7.41 5
1.0%

temperature_C
Real number (ℝ)

Distinct448
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.67574
Minimum15
Maximum34.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-06-11T00:02:11.220852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile16.18
Q120.295
median24.655
Q329.09
95-th percentile33.3915
Maximum34.84
Range19.84
Interquartile range (IQR)8.795

Descriptive statistics

Standard deviation5.3488986
Coefficient of variation (CV)0.2167675
Kurtosis-1.0744021
Mean24.67574
Median Absolute Deviation (MAD)4.425
Skewness0.008958518
Sum12337.87
Variance28.610716
MonotonicityNot monotonic
2025-06-11T00:02:11.443397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.83 3
 
0.6%
29.66 3
 
0.6%
16.18 3
 
0.6%
25.68 2
 
0.4%
29.09 2
 
0.4%
17.01 2
 
0.4%
24.51 2
 
0.4%
17.8 2
 
0.4%
24.1 2
 
0.4%
33.55 2
 
0.4%
Other values (438) 477
95.4%
ValueCountFrequency (%)
15 1
0.2%
15.01 1
0.2%
15.04 1
0.2%
15.11 1
0.2%
15.2 1
0.2%
15.21 1
0.2%
15.23 1
0.2%
15.25 1
0.2%
15.3 1
0.2%
15.39 1
0.2%
ValueCountFrequency (%)
34.84 1
0.2%
34.71 1
0.2%
34.52 1
0.2%
34.44 1
0.2%
34.4 1
0.2%
34.33 2
0.4%
34.19 1
0.2%
34.09 1
0.2%
34.01 1
0.2%
33.95 1
0.2%

rainfall_mm
Real number (ℝ)

Distinct496
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.68574
Minimum50.17
Maximum298.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-06-11T00:02:11.651934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50.17
5-th percentile65.351
Q1119.2175
median191.545
Q3239.035
95-th percentile289.178
Maximum298.96
Range248.79
Interquartile range (IQR)119.8175

Descriptive statistics

Standard deviation72.293091
Coefficient of variation (CV)0.39790185
Kurtosis-1.2095439
Mean181.68574
Median Absolute Deviation (MAD)60.21
Skewness-0.15098075
Sum90842.87
Variance5226.2911
MonotonicityNot monotonic
2025-06-11T00:02:11.856407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.64 2
 
0.4%
88.3 2
 
0.4%
79.51 2
 
0.4%
137.08 2
 
0.4%
277.7 1
 
0.2%
187.46 1
 
0.2%
154.92 1
 
0.2%
118.41 1
 
0.2%
92.69 1
 
0.2%
231.35 1
 
0.2%
Other values (486) 486
97.2%
ValueCountFrequency (%)
50.17 1
0.2%
52.35 1
0.2%
52.36 1
0.2%
52.38 1
0.2%
52.42 1
0.2%
52.64 1
0.2%
55.13 1
0.2%
57.19 1
0.2%
58.47 1
0.2%
59 1
0.2%
ValueCountFrequency (%)
298.96 1
0.2%
298.52 1
0.2%
298.09 1
0.2%
298.08 1
0.2%
297.67 1
0.2%
296.33 1
0.2%
296.11 1
0.2%
295.96 1
0.2%
295.95 1
0.2%
295.74 1
0.2%

humidity_%
Real number (ℝ)

Distinct481
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.19446
Minimum40.23
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-06-11T00:02:12.050073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40.23
5-th percentile42.1025
Q151.865
median65.685
Q377.995
95-th percentile87.381
Maximum90
Range49.77
Interquartile range (IQR)26.13

Descriptive statistics

Standard deviation14.642849
Coefficient of variation (CV)0.22460266
Kurtosis-1.274798
Mean65.19446
Median Absolute Deviation (MAD)12.92
Skewness-0.055725637
Sum32597.23
Variance214.41303
MonotonicityNot monotonic
2025-06-11T00:02:12.287526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.2 3
 
0.6%
73.68 2
 
0.4%
46.45 2
 
0.4%
79.3 2
 
0.4%
81.08 2
 
0.4%
61.31 2
 
0.4%
72.91 2
 
0.4%
88.71 2
 
0.4%
87.38 2
 
0.4%
76.37 2
 
0.4%
Other values (471) 479
95.8%
ValueCountFrequency (%)
40.23 1
0.2%
40.29 1
0.2%
40.42 1
0.2%
40.45 1
0.2%
40.57 1
0.2%
40.59 1
0.2%
40.62 1
0.2%
40.64 1
0.2%
40.78 1
0.2%
40.83 1
0.2%
ValueCountFrequency (%)
90 1
0.2%
89.84 2
0.4%
89.74 1
0.2%
89.42 1
0.2%
89.36 1
0.2%
89.29 1
0.2%
89.15 1
0.2%
89.14 1
0.2%
88.92 1
0.2%
88.82 1
0.2%

sunlight_hours
Real number (ℝ)

Distinct332
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.03014
Minimum4.01
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-06-11T00:02:12.491557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.01
5-th percentile4.4
Q15.6675
median6.995
Q38.47
95-th percentile9.711
Maximum10
Range5.99
Interquartile range (IQR)2.8025

Descriptive statistics

Standard deviation1.6916704
Coefficient of variation (CV)0.24063112
Kurtosis-1.1452539
Mean7.03014
Median Absolute Deviation (MAD)1.385
Skewness0.015783466
Sum3515.07
Variance2.8617489
MonotonicityNot monotonic
2025-06-11T00:02:12.704302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.96 6
 
1.2%
5.82 5
 
1.0%
5.81 4
 
0.8%
4.41 4
 
0.8%
6.3 4
 
0.8%
4.02 4
 
0.8%
5.98 4
 
0.8%
5.66 4
 
0.8%
6.01 4
 
0.8%
5.34 4
 
0.8%
Other values (322) 457
91.4%
ValueCountFrequency (%)
4.01 1
 
0.2%
4.02 4
0.8%
4.03 1
 
0.2%
4.04 1
 
0.2%
4.05 1
 
0.2%
4.08 1
 
0.2%
4.1 1
 
0.2%
4.11 3
0.6%
4.15 1
 
0.2%
4.22 1
 
0.2%
ValueCountFrequency (%)
10 1
 
0.2%
9.98 2
0.4%
9.95 1
 
0.2%
9.94 1
 
0.2%
9.92 1
 
0.2%
9.9 1
 
0.2%
9.89 3
0.6%
9.88 3
0.6%
9.87 1
 
0.2%
9.85 1
 
0.2%

irrigation_type
Categorical

Missing 

Distinct3
Distinct (%)0.9%
Missing150
Missing (%)30.0%
Memory size4.0 KiB
Sprinkler
121 
Manual
118 
Drip
111 

Length

Max length9
Median length6
Mean length6.4028571
Min length4

Characters and Unicode

Total characters2241
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSprinkler
2nd rowDrip
3rd rowSprinkler
4th rowSprinkler
5th rowManual

Common Values

ValueCountFrequency (%)
Sprinkler 121
24.2%
Manual 118
23.6%
Drip 111
22.2%
(Missing) 150
30.0%

Length

2025-06-11T00:02:12.936728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-11T00:02:13.042594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sprinkler 121
34.6%
manual 118
33.7%
drip 111
31.7%

Most occurring characters

ValueCountFrequency (%)
r 353
15.8%
n 239
10.7%
l 239
10.7%
a 236
10.5%
p 232
10.4%
i 232
10.4%
S 121
 
5.4%
k 121
 
5.4%
e 121
 
5.4%
M 118
 
5.3%
Other values (2) 229
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2241
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 353
15.8%
n 239
10.7%
l 239
10.7%
a 236
10.5%
p 232
10.4%
i 232
10.4%
S 121
 
5.4%
k 121
 
5.4%
e 121
 
5.4%
M 118
 
5.3%
Other values (2) 229
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2241
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 353
15.8%
n 239
10.7%
l 239
10.7%
a 236
10.5%
p 232
10.4%
i 232
10.4%
S 121
 
5.4%
k 121
 
5.4%
e 121
 
5.4%
M 118
 
5.3%
Other values (2) 229
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2241
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 353
15.8%
n 239
10.7%
l 239
10.7%
a 236
10.5%
p 232
10.4%
i 232
10.4%
S 121
 
5.4%
k 121
 
5.4%
e 121
 
5.4%
M 118
 
5.3%
Other values (2) 229
10.2%

fertilizer_type
Categorical

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Inorganic
167 
Mixed
167 
Organic
166 

Length

Max length9
Median length7
Mean length7
Min length5

Characters and Unicode

Total characters3500
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOrganic
2nd rowInorganic
3rd rowMixed
4th rowOrganic
5th rowMixed

Common Values

ValueCountFrequency (%)
Inorganic 167
33.4%
Mixed 167
33.4%
Organic 166
33.2%

Length

2025-06-11T00:02:13.187841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-11T00:02:13.304315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
inorganic 167
33.4%
mixed 167
33.4%
organic 166
33.2%

Most occurring characters

ValueCountFrequency (%)
n 500
14.3%
i 500
14.3%
g 333
9.5%
a 333
9.5%
r 333
9.5%
c 333
9.5%
I 167
 
4.8%
o 167
 
4.8%
M 167
 
4.8%
x 167
 
4.8%
Other values (3) 500
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 500
14.3%
i 500
14.3%
g 333
9.5%
a 333
9.5%
r 333
9.5%
c 333
9.5%
I 167
 
4.8%
o 167
 
4.8%
M 167
 
4.8%
x 167
 
4.8%
Other values (3) 500
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 500
14.3%
i 500
14.3%
g 333
9.5%
a 333
9.5%
r 333
9.5%
c 333
9.5%
I 167
 
4.8%
o 167
 
4.8%
M 167
 
4.8%
x 167
 
4.8%
Other values (3) 500
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 500
14.3%
i 500
14.3%
g 333
9.5%
a 333
9.5%
r 333
9.5%
c 333
9.5%
I 167
 
4.8%
o 167
 
4.8%
M 167
 
4.8%
x 167
 
4.8%
Other values (3) 500
14.3%

pesticide_usage_ml
Real number (ℝ)

Distinct469
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.58698
Minimum5.05
Maximum49.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-06-11T00:02:13.470904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.05
5-th percentile6.708
Q114.945
median25.98
Q338.005
95-th percentile47.8305
Maximum49.94
Range44.89
Interquartile range (IQR)23.06

Descriptive statistics

Standard deviation13.202429
Coefficient of variation (CV)0.49657499
Kurtosis-1.2078285
Mean26.58698
Median Absolute Deviation (MAD)11.64
Skewness0.065294352
Sum13293.49
Variance174.30414
MonotonicityNot monotonic
2025-06-11T00:02:14.021068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.28 2
 
0.4%
18.58 2
 
0.4%
21.59 2
 
0.4%
9.11 2
 
0.4%
12.53 2
 
0.4%
49.73 2
 
0.4%
21.62 2
 
0.4%
16.02 2
 
0.4%
10.16 2
 
0.4%
43.3 2
 
0.4%
Other values (459) 480
96.0%
ValueCountFrequency (%)
5.05 1
0.2%
5.07 1
0.2%
5.3 1
0.2%
5.32 1
0.2%
5.36 1
0.2%
5.42 1
0.2%
5.51 1
0.2%
5.57 1
0.2%
5.62 1
0.2%
5.63 1
0.2%
ValueCountFrequency (%)
49.94 1
0.2%
49.93 1
0.2%
49.91 1
0.2%
49.88 1
0.2%
49.78 1
0.2%
49.73 2
0.4%
49.68 1
0.2%
49.66 1
0.2%
49.48 1
0.2%
49.46 1
0.2%
Distinct84
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2024-01-01 00:00:00
Maximum2024-03-28 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-11T00:02:14.220997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T00:02:14.446110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct123
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2024-04-09 00:00:00
Maximum2024-08-17 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-11T00:02:14.660598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T00:02:14.871031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

total_days
Real number (ℝ)

Distinct61
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.496
Minimum90
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-06-11T00:02:15.060732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile94
Q1105.75
median119
Q3134
95-th percentile146
Maximum150
Range60
Interquartile range (IQR)28.25

Descriptive statistics

Standard deviation16.798046
Coefficient of variation (CV)0.14057412
Kurtosis-1.154153
Mean119.496
Median Absolute Deviation (MAD)14
Skewness0.057205139
Sum59748
Variance282.17433
MonotonicityNot monotonic
2025-06-11T00:02:15.254317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
115 18
 
3.6%
131 15
 
3.0%
102 13
 
2.6%
97 13
 
2.6%
107 12
 
2.4%
119 12
 
2.4%
133 12
 
2.4%
106 12
 
2.4%
145 12
 
2.4%
101 12
 
2.4%
Other values (51) 369
73.8%
ValueCountFrequency (%)
90 5
 
1.0%
91 7
1.4%
92 6
1.2%
93 3
 
0.6%
94 10
2.0%
95 5
 
1.0%
96 9
1.8%
97 13
2.6%
98 7
1.4%
99 8
1.6%
ValueCountFrequency (%)
150 7
1.4%
149 5
1.0%
148 3
 
0.6%
147 8
1.6%
146 3
 
0.6%
145 12
2.4%
144 10
2.0%
143 6
1.2%
142 10
2.0%
141 5
1.0%

yield_kg_per_hectare
Real number (ℝ)

Unique 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4032.9269
Minimum2023.56
Maximum5998.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-06-11T00:02:15.454124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2023.56
5-th percentile2200.5295
Q12994.82
median4071.69
Q35062.11
95-th percentile5837.0905
Maximum5998.29
Range3974.73
Interquartile range (IQR)2067.29

Descriptive statistics

Standard deviation1174.433
Coefficient of variation (CV)0.29121109
Kurtosis-1.2585363
Mean4032.9269
Median Absolute Deviation (MAD)1040.735
Skewness-0.028317805
Sum2016463.5
Variance1379293
MonotonicityNot monotonic
2025-06-11T00:02:15.670737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5891.4 1
 
0.2%
4408.07 1
 
0.2%
5389.98 1
 
0.2%
2931.16 1
 
0.2%
2594.9 1
 
0.2%
3715.81 1
 
0.2%
3555.39 1
 
0.2%
3684.22 1
 
0.2%
3882.79 1
 
0.2%
2023.56 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
2023.56 1
0.2%
2029.16 1
0.2%
2043.13 1
0.2%
2046.41 1
0.2%
2049.06 1
0.2%
2050.61 1
0.2%
2066.52 1
0.2%
2066.9 1
0.2%
2067.56 1
0.2%
2077.58 1
0.2%
ValueCountFrequency (%)
5998.29 1
0.2%
5980.83 1
0.2%
5978.14 1
0.2%
5970.39 1
0.2%
5960.64 1
0.2%
5957.61 1
0.2%
5954.03 1
0.2%
5953.33 1
0.2%
5952.62 1
0.2%
5924.44 1
0.2%

sensor_id
Text

Unique 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2025-06-11T00:02:16.104557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters4000
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)100.0%

Sample

1st rowSENS0001
2nd rowSENS0002
3rd rowSENS0003
4th rowSENS0004
5th rowSENS0005
ValueCountFrequency (%)
sens0005 1
 
0.2%
sens0500 1
 
0.2%
sens0001 1
 
0.2%
sens0002 1
 
0.2%
sens0485 1
 
0.2%
sens0486 1
 
0.2%
sens0487 1
 
0.2%
sens0488 1
 
0.2%
sens0489 1
 
0.2%
sens0490 1
 
0.2%
Other values (490) 490
98.0%
2025-06-11T00:02:16.687597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 1000
25.0%
0 699
17.5%
E 500
12.5%
N 500
12.5%
1 200
 
5.0%
3 200
 
5.0%
2 200
 
5.0%
4 200
 
5.0%
5 101
 
2.5%
8 100
 
2.5%
Other values (3) 300
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1000
25.0%
0 699
17.5%
E 500
12.5%
N 500
12.5%
1 200
 
5.0%
3 200
 
5.0%
2 200
 
5.0%
4 200
 
5.0%
5 101
 
2.5%
8 100
 
2.5%
Other values (3) 300
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1000
25.0%
0 699
17.5%
E 500
12.5%
N 500
12.5%
1 200
 
5.0%
3 200
 
5.0%
2 200
 
5.0%
4 200
 
5.0%
5 101
 
2.5%
8 100
 
2.5%
Other values (3) 300
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1000
25.0%
0 699
17.5%
E 500
12.5%
N 500
12.5%
1 200
 
5.0%
3 200
 
5.0%
2 200
 
5.0%
4 200
 
5.0%
5 101
 
2.5%
8 100
 
2.5%
Other values (3) 300
 
7.5%
Distinct172
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2024-01-03 00:00:00
Maximum2024-08-12 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-11T00:02:16.860246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-11T00:02:17.071227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

latitude
Real number (ℝ)

Unique 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.442473
Minimum10.004243
Maximum34.981531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-06-11T00:02:17.300044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.004243
5-th percentile11.350755
Q116.263202
median21.981743
Q328.528948
95-th percentile33.742587
Maximum34.981531
Range24.977288
Interquartile range (IQR)12.265746

Descriptive statistics

Standard deviation7.2834918
Coefficient of variation (CV)0.32454052
Kurtosis-1.2280997
Mean22.442473
Median Absolute Deviation (MAD)6.186541
Skewness0.028356492
Sum11221.237
Variance53.049253
MonotonicityNot monotonic
2025-06-11T00:02:17.543271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.455532 1
 
0.2%
14.970941 1
 
0.2%
16.613022 1
 
0.2%
19.503156 1
 
0.2%
13.307395 1
 
0.2%
28.231214 1
 
0.2%
33.941965 1
 
0.2%
32.524945 1
 
0.2%
17.482124 1
 
0.2%
18.213795 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
10.004243 1
0.2%
10.066893 1
0.2%
10.102747 1
0.2%
10.116728 1
0.2%
10.129536 1
0.2%
10.130335 1
0.2%
10.1331 1
0.2%
10.141128 1
0.2%
10.241808 1
0.2%
10.296576 1
0.2%
ValueCountFrequency (%)
34.981531 1
0.2%
34.899235 1
0.2%
34.811812 1
0.2%
34.806428 1
0.2%
34.774651 1
0.2%
34.672063 1
0.2%
34.589477 1
0.2%
34.533387 1
0.2%
34.52048 1
0.2%
34.395988 1
0.2%

longitude
Real number (ℝ)

Unique 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.392248
Minimum70.020021
Maximum89.991901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-06-11T00:02:17.741444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum70.020021
5-th percentile70.970664
Q175.374713
median80.650284
Q385.654629
95-th percentile89.123329
Maximum89.991901
Range19.97188
Interquartile range (IQR)10.279917

Descriptive statistics

Standard deviation5.9106641
Coefficient of variation (CV)0.073522812
Kurtosis-1.2461011
Mean80.392248
Median Absolute Deviation (MAD)5.157479
Skewness-0.070052765
Sum40196.124
Variance34.93595
MonotonicityNot monotonic
2025-06-11T00:02:17.973391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.880605 1
 
0.2%
82.997689 1
 
0.2%
70.869009 1
 
0.2%
79.068206 1
 
0.2%
70.767329 1
 
0.2%
73.737205 1
 
0.2%
85.854259 1
 
0.2%
73.20625 1
 
0.2%
78.566829 1
 
0.2%
77.077855 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
70.020021 1
0.2%
70.105024 1
0.2%
70.117979 1
0.2%
70.13146 1
0.2%
70.257953 1
0.2%
70.277518 1
0.2%
70.328707 1
0.2%
70.353408 1
0.2%
70.358304 1
0.2%
70.429646 1
0.2%
ValueCountFrequency (%)
89.991901 1
0.2%
89.97974 1
0.2%
89.974564 1
0.2%
89.954055 1
0.2%
89.874704 1
0.2%
89.812315 1
0.2%
89.801528 1
0.2%
89.692255 1
0.2%
89.673368 1
0.2%
89.672164 1
0.2%

NDVI_index
Real number (ℝ)

Distinct61
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60206
Minimum0.3
Maximum0.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-06-11T00:02:18.204540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.33
Q10.4475
median0.61
Q30.75
95-th percentile0.87
Maximum0.9
Range0.6
Interquartile range (IQR)0.3025

Descriptive statistics

Standard deviation0.17540208
Coefficient of variation (CV)0.29133654
Kurtosis-1.2385683
Mean0.60206
Median Absolute Deviation (MAD)0.15
Skewness-0.053378108
Sum301.03
Variance0.030765888
MonotonicityNot monotonic
2025-06-11T00:02:18.393948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.59 15
 
3.0%
0.74 13
 
2.6%
0.35 13
 
2.6%
0.76 13
 
2.6%
0.38 12
 
2.4%
0.41 12
 
2.4%
0.66 12
 
2.4%
0.88 12
 
2.4%
0.47 12
 
2.4%
0.68 12
 
2.4%
Other values (51) 374
74.8%
ValueCountFrequency (%)
0.3 7
1.4%
0.31 5
 
1.0%
0.32 4
 
0.8%
0.33 10
2.0%
0.34 9
1.8%
0.35 13
2.6%
0.36 7
1.4%
0.37 10
2.0%
0.38 12
2.4%
0.39 11
2.2%
ValueCountFrequency (%)
0.9 5
1.0%
0.89 5
1.0%
0.88 12
2.4%
0.87 7
1.4%
0.86 10
2.0%
0.85 10
2.0%
0.84 7
1.4%
0.83 11
2.2%
0.82 5
1.0%
0.81 6
1.2%

crop_disease_status
Categorical

Missing 

Distinct3
Distinct (%)0.8%
Missing130
Missing (%)26.0%
Memory size4.0 KiB
Severe
133 
Mild
125 
Moderate
112 

Length

Max length8
Median length6
Mean length5.9297297
Min length4

Characters and Unicode

Total characters2194
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMild
2nd rowMild
3rd rowSevere
4th rowMild
5th rowMild

Common Values

ValueCountFrequency (%)
Severe 133
26.6%
Mild 125
25.0%
Moderate 112
22.4%
(Missing) 130
26.0%

Length

2025-06-11T00:02:18.577520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-11T00:02:18.693981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
severe 133
35.9%
mild 125
33.8%
moderate 112
30.3%

Most occurring characters

ValueCountFrequency (%)
e 623
28.4%
r 245
 
11.2%
M 237
 
10.8%
d 237
 
10.8%
S 133
 
6.1%
v 133
 
6.1%
i 125
 
5.7%
l 125
 
5.7%
o 112
 
5.1%
a 112
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2194
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 623
28.4%
r 245
 
11.2%
M 237
 
10.8%
d 237
 
10.8%
S 133
 
6.1%
v 133
 
6.1%
i 125
 
5.7%
l 125
 
5.7%
o 112
 
5.1%
a 112
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2194
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 623
28.4%
r 245
 
11.2%
M 237
 
10.8%
d 237
 
10.8%
S 133
 
6.1%
v 133
 
6.1%
i 125
 
5.7%
l 125
 
5.7%
o 112
 
5.1%
a 112
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2194
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 623
28.4%
r 245
 
11.2%
M 237
 
10.8%
d 237
 
10.8%
S 133
 
6.1%
v 133
 
6.1%
i 125
 
5.7%
l 125
 
5.7%
o 112
 
5.1%
a 112
 
5.1%

Interactions

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Correlations

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NDVI_indexcrop_disease_statuscrop_typefertilizer_typehumidity_%irrigation_typelatitudelongitudepesticide_usage_mlrainfall_mmregionsoil_moisture_%soil_pHsunlight_hourstemperature_Ctotal_daysyield_kg_per_hectare
NDVI_index1.0000.0930.0440.000-0.0260.019-0.034-0.0940.0160.0940.028-0.0130.0930.000-0.036-0.0700.037
crop_disease_status0.0931.0000.0000.0000.0000.0000.0770.0490.0000.0860.0000.0000.0790.0810.0000.0000.148
crop_type0.0440.0001.0000.0750.0000.0000.0000.0490.0000.0740.0000.0000.0000.0000.0000.0000.000
fertilizer_type0.0000.0000.0751.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0820.000
humidity_%-0.0260.0000.0000.0001.0000.000-0.0180.0120.030-0.0230.0090.0080.0040.015-0.029-0.0150.042
irrigation_type0.0190.0000.0000.0000.0001.0000.0000.0000.1210.0000.0000.0860.0000.0000.0510.0000.031
latitude-0.0340.0770.0000.000-0.0180.0001.000-0.026-0.1420.0660.075-0.016-0.046-0.0250.0500.044-0.039
longitude-0.0940.0490.0490.0000.0120.000-0.0261.0000.049-0.0080.0000.015-0.014-0.0040.037-0.0070.026
pesticide_usage_ml0.0160.0000.0000.0000.0300.121-0.1420.0491.0000.0110.0000.017-0.1940.0020.0220.0390.043
rainfall_mm0.0940.0860.0740.000-0.0230.0000.066-0.0080.0111.0000.0000.052-0.0570.0040.028-0.026-0.081
region0.0280.0000.0000.0000.0090.0000.0750.0000.0000.0001.0000.0830.0700.0760.0600.0230.000
soil_moisture_%-0.0130.0000.0000.0000.0080.086-0.0160.0150.0170.0520.0831.0000.0050.0580.0390.008-0.063
soil_pH0.0930.0790.0000.0000.0040.000-0.046-0.014-0.194-0.0570.0700.0051.000-0.0200.0290.0200.025
sunlight_hours0.0000.0810.0000.0000.0150.000-0.025-0.0040.0020.0040.0760.058-0.0201.0000.002-0.0090.019
temperature_C-0.0360.0000.0000.000-0.0290.0510.0500.0370.0220.0280.0600.0390.0290.0021.0000.0490.025
total_days-0.0700.0000.0000.082-0.0150.0000.044-0.0070.039-0.0260.0230.0080.020-0.0090.0491.000-0.008
yield_kg_per_hectare0.0370.1480.0000.0000.0420.031-0.0390.0260.043-0.0810.000-0.0630.0250.0190.025-0.0081.000

Missing values

2025-06-11T00:02:07.420484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-11T00:02:07.775787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-11T00:02:08.039660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

farm_idregioncrop_typesoil_moisture_%soil_pHtemperature_Crainfall_mmhumidity_%sunlight_hoursirrigation_typefertilizer_typepesticide_usage_mlsowing_dateharvest_datetotal_daysyield_kg_per_hectaresensor_idtimestamplatitudelongitudeNDVI_indexcrop_disease_status
0FARM0001North IndiaWheat35.955.9917.7975.6277.037.27NaNOrganic6.342024-01-082024-05-091224408.07SENS00012024-03-1914.97094182.9976890.63Mild
1FARM0002South USASoybean19.747.2430.1889.9161.135.67SprinklerInorganic9.602024-02-042024-05-261125389.98SENS00022024-04-2116.61302270.8690090.58NaN
2FARM0003South USAWheat29.327.1627.37265.4368.878.23DripMixed15.262024-02-032024-06-261442931.16SENS00032024-02-2819.50315679.0682060.80Mild
3FARM0004Central USAMaize17.336.0333.73212.0170.465.03SprinklerOrganic25.802024-02-212024-07-041344227.80SENS00042024-05-1431.07129885.5199980.44NaN
4FARM0005Central USACotton19.375.9233.86269.0955.737.93NaNMixed25.652024-02-052024-05-201054979.96SENS00052024-04-1316.56854081.6917200.84Severe
5FARM0006Central USARice44.915.7824.87238.9583.064.92SprinklerMixed24.002024-01-132024-05-061144383.55SENS00062024-03-1223.22785989.4215680.82NaN
6FARM0007North IndiaSoybean36.287.0421.80123.3847.914.02ManualMixed39.292024-03-042024-07-271454501.20SENS00072024-07-1125.22425573.0567850.76NaN
7FARM0008East AfricaMaize27.105.7222.26296.3380.345.44SprinklerMixed47.612024-01-242024-05-241215264.09SENS00082024-04-3023.31765472.5152100.70Mild
8FARM0009Central USASoybean40.546.3519.24184.8276.505.21ManualInorganic49.782024-03-122024-07-081185598.46SENS00092024-05-0813.02510574.4939470.50Mild
9FARM0010East AfricaRice10.256.9216.1866.8541.575.98SprinklerInorganic35.102024-01-182024-04-25984893.41SENS00102024-03-3124.40529174.8599450.58Severe
farm_idregioncrop_typesoil_moisture_%soil_pHtemperature_Crainfall_mmhumidity_%sunlight_hoursirrigation_typefertilizer_typepesticide_usage_mlsowing_dateharvest_datetotal_daysyield_kg_per_hectaresensor_idtimestamplatitudelongitudeNDVI_indexcrop_disease_status
490FARM0491East AfricaSoybean19.067.2020.93117.1345.965.34SprinklerInorganic6.762024-01-092024-06-061492531.89SENS04912024-01-2226.06032874.9801610.79NaN
491FARM0492North IndiaMaize32.147.4421.49286.6184.205.58SprinklerInorganic45.432024-02-112024-05-14934503.82SENS04922024-02-1230.59829574.3855920.88Mild
492FARM0493Central USAWheat28.817.4630.56245.1345.328.47NaNMixed16.582024-03-212024-07-271284203.51SENS04932024-07-1215.51597675.3758700.65Severe
493FARM0494South IndiaCotton18.116.9522.37275.5444.685.09SprinklerOrganic13.502024-02-242024-06-041014590.26SENS04942024-02-2617.75400687.7050610.79Mild
494FARM0495North IndiaRice12.525.9933.18292.6045.017.16DripMixed9.492024-02-012024-05-08975306.25SENS04952024-05-0830.76292888.6935550.38NaN
495FARM0496Central USARice42.856.7030.8552.3579.587.25ManualMixed8.822024-01-162024-06-021384251.40SENS04962024-05-0830.38662376.1477000.59Mild
496FARM0497North IndiaSoybean34.226.7517.46256.2345.145.78NaNOrganic5.052024-01-012024-04-141043708.54SENS04972024-01-1918.83274875.7369240.85Severe
497FARM0498North IndiaCotton15.935.7217.03288.9657.877.69DripInorganic46.552024-01-022024-05-091282604.41SENS04982024-04-2023.26201681.9922300.71Mild
498FARM0499Central USASoybean38.616.2017.08279.0673.099.60DripOrganic43.782024-01-252024-06-041312586.36SENS04992024-03-0219.76498984.4268690.77Severe
499FARM0500North IndiaWheat30.227.4220.5772.6189.745.09NaNInorganic19.372024-02-162024-06-291345891.40SENS05002024-05-1113.45553288.8806050.85Severe